A molecular fuzzy decision-making model for optimizing renewable energy investments towards carbon neutrality

Identifying the most important factors is necessary to determine which areas should be given priority in the energy transition. In this way, it is possible to increase the efficiency of investments by using resources effectively. However, there are limited studies in the literature focusing on this issue. Hence, a new study is needed to determine the most important factors affecting the success of renewable energy integration. Accordingly, the purpose of this study is to find the most critical renewable energy investment strategies to implement effective carbon neutrality policies. A new model is generated to reach this objective. Firstly, to define expert prioritization, an evaluation is conducted by artificial intelligence. Secondly, selected indicators are weighted via molecular fuzzy cognitive maps. Thirdly, alternative strategies of carbon neutrality policies are ranked by fuzzy molecular ranking. The main contribution of this study is that effective investment policies related to renewable energy integration can be determined for successful carbon neutrality policies by created a novel model. The most significant superiority of this model is that fuzzy decision-making methodology is integrated with molecular geometry science. In this process, by computing the degrees with different geometrical shapes, uncertainties in the evaluation process can be handled more effectively. The findings denote that technological infrastructure is the most critical performance indicator of renewable energy integration projects. Similarly, economic feasibility is found as the second most essential determinant of this situation. On the other hand, setting the long-term contracts with renewable producers is the most essential investment alternative to implement effective carbon neutrality policies.